The government Q&A system can handle user queries in real-time, improving the efficiency of businesses and the public, while reducing the pressure of manual consultation. However, the service scenarios of the government Q&A system are diverse and require accurate and standardized expression of answers. Existing methods, which either utilize preset knowledge bases to generate answers or language models with limited scale, are unable to effectively understand consultations and generate trustworthy answers that are accurate and interpretable across multiple service scenarios. Therefore, this study proposes a government Q&A system based on a large language model to provide trustworthy government responses. The method employs a large language model specific to government service as the core module for content understanding and answer generation, assisted by an analysis guidance module and a domain knowledge base module. When generating answers, the large language model references the consulting analysis results provided by the analysis guidance module and the domain knowledge offered by the domain knowledge base module to produce answers that are accurate and consistent with the facts. The reference information during answer generation serves as a foundation to enhance the interpretability of the answers. A comprehensive dataset, containing multi-level and multi-granularity government public information, is collected and organized to construct the modules involved in the method and to test their effectiveness. This dataset includes 1901 documents and 10503 question-answer pairs. Finally, experiments verify that the prototype system, implemented based on the proposed method, can generate accurate and interpretable answers for user inquiries in multiple service scenarios, proving the effectiveness of each module in the system.